Title: Class 9 Analyzing Pretest Data, Modifying Measures, Keeping Track of Measures, Creating Scale Scores
1Class 9 Analyzing Pretest Data, Modifying
Measures,Keeping Track of Measures, Creating
Scale Scores November 15, 2007
- Anita L. Stewart
- Institute for Health Aging
- University of California, San Francisco
2Overview of Class 9
- Analyzing pretest data
- Modifying/adapting measures
- Keeping track of your study measures
- Creating and testing scales in your sample
3Summarize Data on Pretest Interviews
- Summarize problems and nature of problems for
each item - Determine how important problems are
- Results become basis for possible
revisions/adaptations
4Methods of Analysis
- Optimal transcripts of all pretest interviews
- For each item - summarize all problems
- During standard administration
- Responses to specific probes
- Types of problems
- Interviewer problems
- Respondent problems
5Methods of Analysis
- Analyze dialogue (narrative) for clues to solve
problems - During standard administration
- Responses to specific probes
6Behavioral Coding
- Complements cognitive interviews
- Systematic approach to identifying problems with
items - interviewer and respondent problems
7Examples of Interviewer Behaviors Indicating
Problem Items
- Question misread or altered
- Slight change meaning not affected
- Major change alters meaning
- Question skipped
8Examples of Respondent Behaviors Indicating
Problem Items
- Asks for clarification or repeat of question
- Did not understand question
- Doesnt know the answer
- Qualified answer (e.g., it depends)
- Indicates answer falls between existing response
choices - Refusal
9Summarize Behavioral Coding For Each Item
- Proportion of interviews with each problematic
behavior - For standard administration
- of occurrences of each problem divided by N,
e.g., 7/48 respondents requested clarification
10Behavioral Coding Summary Sheet Standard
Administration (N20)
11Additional Behavioral Codes Based on Probes
- Respondents may appear to answer question
appropriately - Additional problems identified with probes
12Examples of Behavioral Codes Based on Probes
- Probe on meaning
- Response indicates lack of understanding
- Probe on use of response options
- Response indicates options are problematic
13Behavioral Coding of Probe Results
- I asked you how often doctors asked you about
your health beliefs. What does the term health
beliefs mean to you? - Behavioral coding times response indicated
lack of understanding as intended - e.g., 2/15 respondents did not understand meaning
based on response to probe
14Behavioral Coding Summary Sheet Standard
Administration (N20) Probes
15Interpret Results
- Determine if problem is common
- Items with only a few problems may be fine
- Items are questionable when
- several types of problems were found
- several subjects experienced the same problem
- Another approach
- Problem identified in gt15 of interviews as
criterion for further exploration
16Interpret Results (cont.)
- Determine if common problems with an item are
serious - Gross misunderstanding of the question
- Yields completely erroneous answer
- Couldnt answer the question at all
- Some less serious common problems can be
addressed by improved instructions or a slight
modification -
17Behavioral Coding Identifies Problem Items
- Solution not always obvious
- How to determine ways to modify the items
18Content Analysis of Entire Interview
- Use qualitative analysis software (e.g., NVIVO)
- Review all dialogue that ensued during
administration of structured items and open-ended
probes - can reveal source of problems
- can help in deciding whether to keep, modify or
drop items
19Results Probing Meaning and Cultural
Appropriateness
- I asked you how often doctors asked you about
your health beliefs? What does the term health
beliefs mean to you? - .. I dont want medicine
- .. How I feel, if I was exercising
- .. Like religion? --not believing in
going to doctors?
20Results Probing Meaning and Cultural
Appropriateness
- I asked you how often doctors asked you about
your health beliefs? What does the term health
beliefs mean to you? - .. I dont want medicine
- .. How I feel, if I was exercising
- .. Like religion? --not believing in
going to doctors? - We changed the question to personal beliefs
about your health
21Results Probing the Meaning of a Phrase
- What does the phrase office staff mean to
you? - the receptionist and the nurses
- nurses and appointment people
- the person who takes your blood
pressure and the clerk in the front
office
22Modification Probing the Meaning of a Phrase
- What does the phrase office staff mean to you?
- the receptionist and the nurses
- nurses and appointment people
- the person who takes your blood
pressure and the clerk in the front
office - We changed the question to receptionist and
appointment staff
23Other Examples
- On about how many of the past 7 days did you eat
foods that are high in fiber, like whole grains,
raw fruits, and raw vegetables? - Probe what does the term high fiber mean to
you?
24Other Examples
- On about how many of the past 7 days did you eat
foods that are high in fiber, like whole grains,
raw fruits, and raw vegetables? - Behavioral Coding of item
- Over half of respondents exhibited a problem
- Review answers to probe
- Over ΒΌ did not understand the term
Blixt S et al., Proceedings of section on survey
research methods,American Statistical
Association, 19931442.
25Other Examples (2)
- I seem to get sick a little easier than other
people (definitely true, mostly true, mostly
false, definitely false) - Behavioral coding of item
- Very few problems
Blixt S et al., Proceedings of section on survey
research methods,American Statistical
Association, 19931442.
26Other Examples (2)
- I seem to get sick a little easier than other
people (definitely true, mostly true, mostly
false, definitely false) - Review answers to probe
- Almost 3/4 had comprehension problems
- Most problems around term mostly (either its
true or its not)
27Exploring Differences by Diverse Groups
- Back to issues of equivalence of meaning across
groups - All cognitive interview analyses can be done
separately by group
28Results Use of Response Scale
- Do diverse groups use the response scale in
similar ways? - Re questions about cultural competence of
providers - Interviewers reported that Asian respondents who
were completely satisfied did not like to use the
highest score on the rating scale
CPEHN Report, 2001
29Results Probe on DifficultyCES-D Item
- During the past week, how often have you felt
that you could not shake off the blues, even with
help from family and friends - Probe Do you feel this is a question that people
would or would not have difficulty understanding? - Latinos more likely than other groups to report
people would have difficulty
TP Johnson, Health Survey Research Methods, 1996
30Use of Response Scale (Not in Pretest)
- In an exercise class of Samoans, instructor asked
them to rate the difficulty of the exercise he
just did on a 1-10 scale - They did not understand what he meant by a 1-10
scale - Western metric?
31Overview of Class 9
- Analyzing pretest data
- Modifying/adapting measures
- Keeping track of your study measures
- Creating and testing scales in your sample
32Now What!
- Issues in adapting measures based on pretest
results
33Switzer et al. reading
- From class 3 section of class binder
- p 405-406 modifying measures
34Criteria for Whether or Not to Modify Measure
- Contact author
- May be open to modifications, working with you
- Be sure your opinion is based on extensive
pretests with consistent problems - Dont rely on a few comments in a small pretest
- Work with a measurement specialist to assure that
proposed modifications are likely to solve problem
35Tradeoffs of Using Adapted Measures
- Advantages
- Improve internal validity
- Disadvantages
- Lose external validity
- Know less about modified measure
- Need to defend new measure
36Strategies for Modifying
- Retain original intact items (if feasible)
- Add modified items
- New items
- Slight modifications
- If modifications are extensive
- Pretest your new items
37Modifying response categories
- If response choices are too few and/or coarse,
can improve without compromising too much - Try adding levels within existing response scale
38One Modification Too Many Response Choices
- SF36 version 1
- 1 - All of the time
- 2 - Most of the time
- 3 - A good bit of the time
- 4 - Some of the time
- 5 - A little of the time
- 6 - None of the time
- SF36 version 2
- 1 - All of the time
- 2 - Most of the time
- 3 - Some of the time
- 4 - A little of the time
- 5 - None of the time
39Modification of Health Perceptions Response
Choices for Thai Translation
- Usual responses
- 1 - Definitely true
- 2 - Mostly true
- 3 - Dont know
- 4 - Mostly false
- 5 - Definitely false
- Modified
- 1 Not at all true
- 2 A little true
- 3 - Somewhat true
- 4 - Mostly true
- 5 Definitely true
e.g., My health is excellent, I expect my health
to get worse
40Modifying Item Stems
- If item wording will not be clear to your
population - Can add parenthetical phrases
- Have you ever been told by a doctor that you have
diabetes (high blood sugar)?
41Writing New Items
- One approach if you find serious problems with a
standard measure - Write new items that you think will be better
- Same format
- Always include entire original measure (if
feasible) - New items are extra
42Strategy for Modified Measures
- Test measure in original and adapted form
- Choose measure that performs the best
43Analyzing New Measure (Scale)
- Factor analysis
- All original items
- Original plus new items replacing original
- Correlations with other variables
- Does the new measure detect stronger
associations? - Outcome measure
- Does the new measure detect more change over
time?
44Overview of Class 9
- Analyzing pretest data
- Modifying/adapting measures
- Keeping track of your study measures
- Creating and testing scales in your sample
45Questionnaire Guides
- Organizing your survey data and measures
- Way to keep track of measurement decisions
- Documents sources of measures before you forget
- Any modifications
46See Sample Guide to Measures Used in
Questionnaire/Survey Handout
- Type of variable
- Concept
- Measure
- Data source
- Number of items/survey question numbers
- Number of scores or scales for each measure
- References
47Codebook See Sample Questionnaire Guide
Summary of Variables.. Handout
- Develop codebook of scoring rules
- Several purposes
- Variable list
- Meaning of scores
- Special coding
- How you want missing data handled
48Item Naming Conventions
- Optimal coding is to assign raw items their
questionnaire number - Can always link back to questionnaire easily
- Some people assign a variable name to the
questionnaire item - This will drive you crazy
49Variable Naming Conventions
- Assigning variable names is an important step
- make them as meaningful as possible
- plan them for all questionnaires at the beginning
- For study with more than one source of data, a
suffix can indicate which point in time and which
questionnaire - B for baseline, 6 for 6-month, Y for one year
- M for medical history, L for lab tests
50Variable Naming Conventions (cont)
- Medical History Questionnaire
- HYPERTMB HYPERTM6
- Baseline 6 months
51Variable Naming Conventions (cont)
- A prefix can help sort variable groupings
alphabetically - e.g., S for symptoms
- SPAINB, SFATIGB, SSOBB
-
52Overview of Class 9
- Analyzing pretest data
- Modifying/adapting measures
- Keeping track of your study measures
- Creating and testing scales in your sample
53On to Your Field Test or Study
- What to do once you have your baseline data
- How to create summated scale scores
54Preparing Surveys for Data Entry 4 Steps
- Review surveys for data quality
- Reclaim missing and ambiguous data
- Address ambiguities in the questionnaire prior to
data entry - Code open-ended items
55Review Surveys for Data Quality
- Examine each survey in detail as soon as it is
returned, and mark any.. - Missing data
- Inconsistent or ambiguous answers
- Skip patterns that were not followed
56Reclaim Missing and Ambiguous Data
- Go over problems with respondent
- If survey returned in person, review then
- If mailed, call respondent ASAP, go over missing
and ambiguous answers - If you cannot reach by telephone, make a copy for
your files and mail back the survey with request
to clarify missing data
57Address Ambiguities in the Questionnaire Prior to
Data Entry
- When two choices are circled for one question,
randomly choose one (flip a coin) - Clarify entries that might not be clear to data
entry person
58Code Open-Ended Items
- Open-ended responses have no numeric code
- e.g., name of physician, reason for visiting
physician - Goal of coding open-ended items
- create meaningful categories from variety of
responses - minimize number of categories for better
interpretability - Assign a numeric score for data entry
59Example of Open-Ended Responses
- 1.What things do you think are important for
doctors at this clinic to do to give you high
quality care? - Listen to your patients more often
- Pay more attention to the patient
- Not to wait so long
- Be more caring toward the patient
- Not to have so many people at one time
- Spend more time with the patients
- Be more understanding
60Process of Coding Open-Ended Data
- Develop classification scheme
- Review responses from 25 or more questionnaires
- Begin a classification scheme
- Assign unique numeric codes to each category
- Maintain a list of codes and the verbatim answers
for each - Add new codes as new responses are identified
- If a response cannot be classified, assign a
unique code and address it later
61Example of Open-Ended Codes
- Communication 1
- Listen to your patients more often 1
- Pay more attention to the patient 1
- Access to care 2
- Not to wait so long 2
- Not to have so many people at one time 2
- Allow more time 3
- Spend more time with the patients 3
- Emotional Support 4
- Be more understanding 4
- Be more caring toward the patient
62Verify Assigned Codes
- Ideally, have a second person independently
classify each response according to final codes - Investigator can review a small subset of
questionnaires to assure that coding assignment
criteria are clear and are being followed
63Reliability of Open-Ended Codes
- Depends on quality of question, of codes
assigned, and the training and supervision of
coders - Initial coder and second coder should be
concordant in over 90 of cases
64Data Entry
- Set up file
- Double entry of about 10 of surveys
- SAS or SPSS will compare two for accuracy
- Acceptable 0-5 error
- If 6 or greater consider re-entering data
65Print Frequencies of Each Item and Review Range
Checks
- Verify that responses for each item are within
acceptable range - Out of range values can be checked on original
questionnaire - corrected or considered missing
- Sometimes out of range values mean that an item
has been entered in the wrong column - a check on data entry quality
66Print Frequencies of Each Item and Review
Consistency Checking
- Determine that skip patterns were followed
- Number of responses within a skip pattern need to
equal number who answered skip in question
appropriately
67Print Frequencies of Each Item and Review
Consistency Checking (cont.)
- 1. Did your doctor prescribe any medications?
(yes, no) - 1a. If yes, did your doctor explain the side
effects of the medication? - If 75 respondents (of 90) said yes to 1, expect
75 responses to question 1a. - Often will find that more people(e.g., 80)
answered the second question than were supposed to
68Print Frequencies of Each Item and Review
Consistency Checking (cont.)
- Go back to a questionnaires of those with
problems - check whether initial filter item was
incorrectly answered or whether respondent
inadvertently answered subset - sometimes you wont know which was correct
- Hopefully this was caught during initial review
of questionnaire and corrected by asking
respondent
69Deriving Scale Scores
- Create scores with computer algorithms in SAS,
SPSS, or other program - Review scores to detect programming errors
- Revise computer algorithms as needed
- Review final scores
70Creating Likert Scale Scores
- Translate codebook scoring rules into program
code (SAS, SPSS) - Reverse all items as specified
- Apply scoring rules
- Apply missing data rules
- Sample for SAS (see handout)
71Testing Scaling Properties and Reliability in
Your Sample for Multi-Item Scales
- Obtain item-scale correlations
- Part of internal consistency reliability program
- Calculate reliability in your sample (regardless
of known reliability in other studies) - internal-consistency for multi-item scales
- test-retest if you obtained it
72SAS Chapter 3 Assessing Reliability with
Coefficient Alpha
- Review statements and output
- How to test your scales for internal consistency
and appropriate item-scale correlations
73SAS Chapter 3 Assessing Scale Reliability with
Coefficient Alpha
- PROC CORR
- DATAdata-set-name
- ALPHA
- NOMISS
- VAR (list of variables)
- Output
- Coefficient alpha
- Item correlations
- Item-scale correlations corrected for overlap
74Testing Reliability in STATA
- www.stata.com/help.egi?alpha
- Alpha varlist if in , options
- SEE HANDOUT
75What if Reliability is Too Low?
- Have to decide if you need to modify a scale
- New scales under development
- Modify using item-scale criteria
- Standard scales cannot change
- Simply report problems as caveats in your
analyses - If problem is substantial
- Can create a modified scale and report results
using standard and modified scale
76Homework for Class 10
- Summarize briefly your pretest results
- Indicate whether the measure appears to be
appropriate for the people in your pretest - No inferences to broader sample needed
.